#and my colleagues are- incredibly- STILL ARGUING about the CONTENT of the labels

[goddamnshinyrock]

I’m going to dream of bleeds and crop marks and margins and fucking kerning tonight

[souljoon]

Like a fool (pt.1)

pairing: teacher!jungkook x cafe owner!reader word count: 2k genre: fluff, smut, ex lovers au warnings: sexual content, slight dom!jungkook, sub!reader, unprotected sex (stay safe loves!)

synopsis: Everyone in the neighborhood knows you and Jungkook will inevitably end up in a wedlock despite the complicated status of your on-and-off relationship. While you want to keep a civil relationship with him, Jungkook learned not to care about labels long ago since the first time you two broke up. Whatever you’d say, he intends to keep his place in your heart taken for the rest of your life.

~~~

“Your beau is here,” Jimin prompted behind you.

Without turning in his direction, your attention to the carrot cake remaining glued to the carrot cake you were currently frosting about.

At this point, you wouldn’t even be surprised to see Jungkook on the opposite side of your bed in the morning. He seems to find it amusing to see you evidently pissed whenever he’s around. How couldn’t you? He not only takes over Jimin’s job but he often causes a scene with the customers in the cafe—which, to fuel more of your unspoken frustration, are students from the nearby community college.

You couldn’t admit how the attention he was getting from people of different gender identities still bothers you despite the mutual decision to call the engagement off. You understand your ex-fiance is a very attractive man. And his charisma could probably stir attraction from married women regardless of their age.

Over the course of six years of a complicated relationship with him, you two had already been in two break ups in college, citing his laid back self in college and your endless insecurities that urged you to try to get away from him, from the spell he had on you.

While you decided to pursue your dream to start your own cafe business post-graduation, Jungkook surprisingly landed on a teaching job in Jung-do High School which is also located in the same neighborhood two years ago. 

Footsteps are, again, back in the kitchen. “He just wore an apron. So I’m guessing he’s here until the shop closes.” It was Jimin, informing you yet again as if it was part of his job to report Jungkook’s every move to you.

This time, you sweep a brief glance behind. “Don’t let the counter vacant, Chim.” You say, cleaning up the cake board as a finishing touch to your masterpiece.

“He took over the counter, _____. How am I supposed to make him go away when he’s our own human advertisement. He’s attracting more customers!”

With a glare darted to his direction, you suggest, “Then I guess I should replace you with him, instead?”

Jimin visibly sulked, not really wanting to argue with you—his boss. “Fine!”

Six months. That’s how long you’ve been single since. Sure he had you wrapped around his fingers back then. But you wanted to prove to him and to yourself that you can live without him. However, it’s too impossible to keep up with it when he freely deems himself welcome wherever you are, maintaining his act of indifference toward the real score between you.

Intending to place the cake in the display, you finally went out of the kitchen-- ironically, just in time to run into him. Jungkook being the shameless ex-boyfriend that he is, took the cake in your hands.

He was wearing a gym class outfit— a pair of black adidas sweatpants, and a plain, white shirt over a black hoodie. If only you were not trying to stay as far as possible away from him, you’ll probably tease him about his own dress code. He doesn’t look like he just got out of his class as the teacher. He looked like he just went out of bed before he came here.

“Aren’t you supposed to be home?” You ask from behind him.

“I’m bored,” he simply replied.

“What do you mean you’re bored? Haven’t you just got off work?”

He spun around, startling you when you came face to face with him. If you couldn’t properly see his entire face before, you do now much to your annoyance.

He sighs. “I did. Look, I’m just helping Jimin-hyung out here. I won’t bother you, I swear.”

“You don’t have to because you’re not my employee, Kook.”

“Well, I could use some part time if you’re hiring.” Jungkook shrugs.

Here we go, again.

Your eyes narrowed to which roused him to raise his hands up defensively.

“Jagiya—”

“Lovebirds,” Jimin suddenly interrupts.

“What?!” You both snapped back at Jimin’s direction.

“Whoa, tone it down— you two. Restroom is right there in case you need to release the sexual tension. It’s getting intense out here.” He jests, making a shooing motions with his hands.

Jungkook wasted no more time and took it as his cue to grab your wrist, dragging you with him as he navigated the way past the kitchen into the storage room.

A temporary relief washes through you when Jungkook brought you in this enclosed, rather safe space instead of the restroom. However, dread slowly consumes your whole being when you hear the familiar sound of the knob locking.

Jungkook pivoted back, facing you. “Let’s talk here.”

Your eyes lingered down where his hand maintained his grasp around your wrist. “Why? There’s nothing else to talk about.”

“For the umpteenth time, I saw the landlord across the street like he was waiting for someone,”

You look up, quirking up an eyebrow at his sudden shot of a subject relating to Seokjin. “What’s your point?”

“I don’t trust him.” He deduces, childishly.

“What do you want me to do, find another leasing property? This shouldn’t concern you in the first place. You never once heard anything from me about Joohyun.” You mentally cursed, unable to stop yourself from mentioning the name of the woman he was seen in a restaurant a week ago.

“What’s Joohyun got anything to do with this?”

You scoff. “You know what, I don’t need to answer that. We’re not together anymore so it’s none of my business.”

Jungkook seized your attempt to leave, latching onto your arm just in time. As he pulls you back, you were met with the subtle amusement plastered obnoxiously on his face.

“We’re not done here, baby. So... Joohyun, really? My colleague?” A laugh slips out of him, seemingly pleased. You, on the other hand, felt insulted on his take of your serious remark. Your blood started rising up. So the rumors aren’t true?

You jerked away. Well... tried to, because your hand stayed locked around his firm grip. “Let me go, I need to go back to the kitchen.”

You stepped back when he abruptly inched forward. You were puzzled for a second, but when your back touched the surface of the door, you knew you fell from his trap as he steadied himself with his palms pressed flat above your head. You turned your face away, avoiding his heated gaze. But the gesture only gave Jungkook a room to nestle his head on the exposed skin of your neck.

The moment you felt his warm lips touch your skin, you squeezed your eyes shut. “You’ve been pretty good at keeping a safe distance from me, baby. You have no idea how much  I fucking miss you, missed keeping you all to myself like this.” He expresses in a thick, sultry tone.

You shake your head, knowing full well what he meant. “We c-cant, Jimin is--”

“--not here.” He finishes, pressing his lower body against yours and teasing your sweet spot with a gentle suck. The bulge on his mid-region was enough to make your panties wet instantaneously and your body heats up too quickly.

“Jungkook,” his name slips out of your mouth.

“Please tell me you’re still in birth control.” He desperately murmurs against your skin on the curve of your neck.

You frantically bobbed your head, lost at the hot trail of kisses he’s leaving on your skin.

With an eager pull of the strings on his nape and back, he rids the apron off of his front followed by a swift pull of his sweatpants with his boxers, just enough to release his hard member.

Your mouth instantaneously watered at the sight of the maddeningly pink head and aroused length, thick and hard just the way you remembered it the last time Jungkook fucked you. It happened in his car three months ago. You were too intoxicated then to control yourself from jumping up into his lap as he drove you back to your apartment. To keep your pride intact, you tried to steer clear from repeating the same mistake again. Not when you’re not officially back together.

Right now, you’re too sexually neglected to care about anything.

“I want you in my mouth,” you beg, not believing you sounded incredibly hasty than you actually have estimated.

He swats your hand off when he sensed your hand extending towards his crotch, “I’d love to fuck your mouth baby, but we don’t have that much time. I need to be inside your pussy,” You felt his palms scooping you up through your butt, sandwiching you between his body and the door. Your legs automatically weaving around his hips to steady yourself.

Then pushes your underwear aside, “This is probably the only reason why I love you wearing skirts. Easy access—fuck baby, so tight.” He barely sank his cock in, yet you could already feel the sting of your walls as they stretch around him.

Your hand flew to the back of head, eager to bury your fingers beneath his curly locks.

Just as you part your mouth to speak to encourage more his entrance, he suddenly propels his hips forward, pushing his dick to the hilt which roused a cry from you.

“Fucking tight! I’m gonna break you so much you won’t ever forget about me. You understand, darling?”

“Yes, yes, please fuck me!” You cried out, reeling from both the sting of your muscles caused by his forceful entrance, and the familiar warmth filling you full.

Without bothering to warm you up, he began a breathtaking pace despite his overwhelming intrusion. You didn’t mind, though. In fact, his thrusts were making your moans irrepressible and your thighs tremble in delight.

Jungkook places his head between the valleys of your covered mounds, not missing his faint grunts, lost in his own pleasure.

“You like that, huh? You like the idea of being fucked outside, baby girl? I’ve had enough this bullshit,” He growls with a series of rough jerk of his hips, forcing a cry of his name out of you.

“That’s right, moan my name. Just wait until I get you all alone tonight, I’ll make sure you won’t ever think of breaking up with me. Do you hear me?” He warns darkly, emphasizing the severity of his threat with a shove of his dick so deep his tip was heavenly kissing your precious spot from your insides.

“Oh god,” you lamented, deliriously.

You could already feel the building up in your abdomen just as fast as he started rocking into you. You’ve known him long enough for you to easily sense it was the same for him too, concealing his moans with his mouth latched onto your prickly skin.

“That’s right. Come for me!” he grunted in between powerful thrusts.

His command did the trick, sending your body forward as you exploded, your walls tighten around him with each snap of his hips against your pelvis. Soon enough, he jerked off his load inside you with a growl rumbling on his chest.

Grimace creases on your expression as he cautiously pulls his cock out, following his load combined with your juices gushing out of your pussy down to the insides of your thighs.

Barely recovered from the earth shattering orgasm you had for the first time in three months, you heard a series of banging coming from the other side of the door.

“You done, lovebirds?” Your eyes clenched shut in realization, quietly plotting the assassination of some guy named Jimin.

“Thanks for ruining the moment,” Jungkook retorts back. “Not a problem. You guys seriously need to get the fuck out, I ran out of beans in the jar and try not fuck each other here next time, yeah?”

Amused with the scene unfolding, Jungkook casually pushes your underwear back to its place, smoothening your skirt down as if nothing inappropriate had occurred here. He kisses the tip of your nose, before turning the knob of the door.

Couldn’t this get any more embarrassing?

~~~

Thank you for reading and apologies for any spelling/ grammatical errors. I havent edited this yet.  Part 2 will most likely be posted on Monday or Tuesday :)

[isearchgoood]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

via Blogger https://ift.tt/2WiKQZt #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[theinjectlikes2]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

from The Moz Blog https://ift.tt/31N2yFs via IFTTT

[whitelabelseoreseller]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

from The Moz Blog http://tracking.feedpress.it/link/9375/12923731

[bfxenon]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[kjt-lawyers]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[noithatotoaz]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[thanhtuandoan89]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[ductrungnguyen87]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[gamebazu]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

https://ift.tt/2Pj0DGo

[camerasieunhovn]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[drummcarpentry]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[evempierson]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[epackingvietnam]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

#túi_giấy_epacking_việt_nam #túi_giấy_epacking #in_túi_giấy_giá_rẻ #in_túi_giấy #epackingvietnam #tuigiayepacking

[lakelandseo]

Machine Learning 101 - Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn't have to be — and you don't have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week's episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I'm talking about all things machine learning, something, as many of you know, I'm super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven't really reached artificial intelligence. But it's just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it's a bit flipped, and it works extremely well. There are two primary types of machine learning:

You have supervised, which is where you're basically feeding a model labeled training data, 

And then unsupervised, which is where you're feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You'll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there's all of this data that you're putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It's a really important part to understand, because unless you have the right types of data to feed a model, you're not going to get the desired outcome that you would like. 

A machine learning model example

So let's look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it's going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don't want it to do is to fit every single data point and have a line that looks like that — that's also known as overfitting — because it doesn't play nice for new data points. You don't want a model to get so calculated to your dataset that it doesn't predict accurately in the future.

A way to look at that is by the loss function. That's maybe getting a bit deeper in this, but that's how you would measure how the line is being fit. Let's see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don't suggest you do this for primary pages. This isn't going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It's really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what's in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It's pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I've got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you'll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You'll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It's also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it's something to definitely think about. 

TensorFlow is a great resource. It's what Google put out, and it's what a lot of their machine learning models is built off of. They've got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don't care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that's pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can't express that enough. So a lot of machine learning and data scientists, it's all data cleaning and parsing, and that's the bulk of the work in this field.

It's important to be aware of that. So that's it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

If you enjoyed this episode of Whiteboard Friday, you'll be delighted by all the cutting-edge SEO knowledge you'll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney's talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn't love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!